计及需求响应的海岛微电网双层优化运行  

Dual-layer optimization operation of an island microgrid considering demand response

作　　者：匡洪海[1] 徐雨淏 李子龙 杨慧娴 KUANG Honghai;XU Yuhao;LI Zilong;YANG Huixian(College of Electrical and Information Engineering,Hu’nan University of Technology,Zhuzhou,Hu’nan 412007)

机构地区：[1]湖南工业大学电气与信息工程学院,湖南株洲412007

出　　处：《电气技术》2025年第3期15-21,29,共8页Electrical Engineering

基　　金：湖南省自然科学基金项目(2023JJ50176);湖南省教育厅重点项目(23A0441)。

摘　　要：为综合考虑微电网调度过程中供给侧和需求侧的利益,本文建立计及需求响应的海岛微电网双层优化调度模型。该模型上层以微电网的净收益为目标优化各单元出力,下层以居民的综合舒适度为目标优化负荷曲线。采用一种改进的蜣螂优化算法对双层优化模型进行求解。通过Sinusoidal映射初始化种群,并利用准对立学习优化,增加了种群的多样性;在更新阶段引入哈里斯鹰的围攻策略和自适应t分布扰动,增强寻优能力,提高了解的质量;通过测试函数与其他算法进行对比,验证了改进蜣螂优化算法的优越性。算例分析结果表明,利用所提改进蜣螂优化算法求解双层优化模型,可在提高系统经济效益的同时,提升用户的用电舒适度和用能舒适度;与原始蜣螂优化算法进行对比,验证了所提改进算法的有效性。To comprehensively consider the benefits of both the supply and demand sides in the scheduling process of a microgrid,an island microgrid dual-layer optimal scheduling model considering demand response is established.The upper level optimizes the output of each unit with the goal of maximizing the net revenue of the microgrid.The lower level optimizes the load curve with the goal of maximizing residents’overall comfort.An improved dung beetle optimizer is used to solve the dual-layer optimization model.The population is initialized using a sinusoidal mapping and optimized with quasi-oppositional learning to increase population diversity.During the update phase,the Harris hawks’besiege strategy and adaptive t-distribution perturbation are introduced to enhance the optimization capability and improve the solution quality.The superiority of the improved algorithm is verified by comparing its convergence on test functions with other algorithms.The case study results show that the improved algorithm not only improves the system’s economic benefits but also enhances the users’electricity and energy comfort.Comparing the results with those obtained by the original dung beetle optimizer confirms the effectiveness of the im-proved method.

关 键 词：微电网 需求响应 双层优化 蜣螂优化算法 

分 类 号：TM732[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]